A Detailed Survey Study on Classification and Various Attributes of Fake News on Social Media

Abstract

Due to the surge in the usage of social media in the recent past, communication between people has undergone a great change. Users interact with each other by sharing a lot of information about the ongoing trends. But most of the information shared recently is misleading with the spread of false news which is known as fake news. A huge volume of such fake news being spread can lead to many complications. The research field has been concentrating on the inception, spread and consequences more nowadays. Detecting the truthfulness of the news is of great concern. It can face many technical difficulties on many grounds. Usage of online tools has made the generation of content easy and is expanded fast, which can lead to a huge amount of data for analyzing. The online content is divergent, which deals with numerous fields, contributing to the job complication. Only computers are not able to evaluate the truthfulness and purpose; therefore, it is dependent on the person–computer interaction. Sometimes, the information that might be termed as fake by a specialist in the field might delude people. Such availability is in restricted quantity but could be a base for the mutual endeavor. Here, a broad summary of the discoveries pertaining to the news which are false is given. The unfavorable effects of ongoing work on methods to detect such news are demonstrated. The readily available datasets are studied which are used to classify fake news. An assuring solution is recommended to analyze online fake news.